Fanny Dao

16.2k citations
85 papers · 3.9k indexed · h-index 39

Impact in

Papers in

Fanny Dao

82 papers receiving 3.9k citations

Peers

Fanny Dao
Comparison fields: 5 of 134
  • Reproductive Medicine 1.1k
  • Obstetrics and Gynecology 633
  • Cancer Research 646
  • Molecular Biology 2.3k
  • Pathology and Forensic Medicine 355
Replace Benedict B. Benigno with:
Benedict B. Benigno United States
Izhak Haviv Australia
Gian Franco Zannoni Italy
H. Kölbl Germany
Richard J. Edmondson United Kingdom
Nam Hoon Cho South Korea
Josep Castellví Spain
Kentaro Nakayama Japan
E. Neely Atkinson United States
Katherine Moxley United States
Fanny Dao relative to Benedict B. Benigno United States Benedict B. Benigno's profile →
Citations per field
00.5×
Benedict B. Benigno · 1×
Citations per year

Countries citing papers authored by Fanny Dao

Since Specialization
Citations

This map shows the geographic impact of Fanny Dao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fanny Dao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fanny Dao more than expected).

Fields of papers citing papers by Fanny Dao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fanny Dao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fanny Dao. The network helps show where Fanny Dao may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Fanny Dao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fanny Dao Line = papers co-authored together Fanny Dao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20252
3 20251
4 20251
5 20241
6 202227
7 202226
8 202114
9 202151
10 202168
11 20216
12 202147
13 202061
14 202099
15 201961
16 201550
17 2014266
18 201382
19 2012156
20 2009152

About Fanny Dao

Fanny Dao is a scholar working on Reproductive Medicine, Obstetrics and Gynecology, Cancer Research, Molecular Biology and Aging, having authored 85 papers that have together received 3.9k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (29 papers), Ovarian cancer diagnosis and treatment (25 papers), Genomics and Phylogenetic Studies (17 papers), RNA and protein synthesis mechanisms (13 papers), Endometrial and Cervical Cancer Treatments (8 papers), Cancer Genomics and Diagnostics (6 papers), Single-cell and spatial transcriptomics (5 papers) and vaccines and immunoinformatics approaches (5 papers). The work is most often cited by research in Reproductive Medicine (1.1k citations), Obstetrics and Gynecology (633 citations), Cancer Research (646 citations), Molecular Biology (2.3k citations) and Pathology and Forensic Medicine (355 citations). Fanny Dao has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Hao Lv, Hao Lin, Douglas A. Levine, Hui Ding, Robert A. Soslow, Hasan Zulfiqar, Narciso Olvera, Wei Chen, Hui Yang and Richard R. Barakat. Their work appears in journals such as Gynecologic Oncology, Briefings in Bioinformatics, BMC Biology, International Journal of Molecular Sciences and Journal of Clinical Oncology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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